The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically. This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption. A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject. This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.

Background: Medical education now focuses on developing skilled and dependable professionals, with particular attention to the hidden curriculum and its influence on professionalism and humanism. Objective: This scoping review aimed to analyze the available evidence on the benefits and adverse effects of the hidden curriculum in medical education. Methods: A scoping review of the literature available in the indexed databases PubMed, Scopus, ScienceDirect, and Latin American and Caribbean Health Sciences Literature (LILACS) with MeSH (Medical Subject Headings) descriptors was conducted on the effects of the hidden curriculum in medical education between January 2000 and April 2024. A total of 29 papers were selected for the review. Results: Our review included studies from 10 countries, most of which were descriptive and cross-sectional, revealing both positive and negative impacts of the hidden curriculum in medical education. These include the transmission of implicit values and the influence on forming skills and professional identity. It was found that some elements contributed to the integral development of students, and others generated challenges that affected the quality of medical education. Likewise, the need for further research to design implementation strategies in different medical schools was described. Conclusions: The hidden curriculum proves to have both a positive and negative impact on the attitudes and values of medical students. The findings highlight the need to generate greater awareness and proactive strategies in educational institutions to improve the quality of training and promote the holistic development of future health professionals.

Professionalism is a cornerstone of medical education and forms a foundation for ethical healthcare. Its importance is often overlooked because of implicit teaching and inconsistent evaluations arising from a lack of standardized definitions and assessment tools. This scoping review aimed to map the literature on assessing professionalism in undergraduate and graduate medical education over the past decade, identify existing approaches, and highlight knowledge gaps. This scoping review involved a systematic search of PubMed, Lilacs, Scopus, and Web of Science databases for peer-reviewed articles on professionalism in medical education from 2015 to 2025. After the screening process, two independent reviewers evaluated the data, and a third reviewer resolved any disagreements. The data were organized into tables and reported thematically. A total of 584 articles were identified, 72 of which met the inclusion criteria. The definitions of professionalism vary, reflecting cultural and institutional differences. Teaching professionalism encompasses role modeling, structured programs, simulation, and evaluation. However, faculty commitment and resource limitations hinder consistency. Assessment tools like Professionalism Mini-Evaluation Exercise (P-MEX) offer valuable insights but face implementation challenges. Teacher feedback is one of the most commonly used and effective strategies to develop professionalism. Teaching and assessment face challenges such as the lack of standardized definitions, time constraints, cultural resistance, and inconsistencies in evaluators’ judgments. Institutional support and validated tools are essential to address these barriers. Professionalism is essential to medical education, but it faces challenges in defining, teaching, and assessing. Therefore, a standardized yet adaptable definition that considers cultural factors is required. Current assessment methods are subjective, and implementation can be problematic. Evaluating professionalism requires balancing standardization, context, and meaningful feedback. Longitudinal, multi-source, and modern strategies enhance reliability. Future research should validate new methods, explore innovative feedback, and prioritize professionalism in competency-based medical education.

Child malnutrition is a public health problem not only in Colombia but throughout the world, as it increases mortality associated with preventable causes. In Colombia, poverty is one of those causes present mainly in rural areas where one in 10 children suffers from malnutrition. We searched ScienceDirect, PubMed, ClinicalKey, and SciELO for references on child malnutrition, its social determinants, and elements for its intervention in Colombia. The review allowed us to identify that many factors influence this problem determined not only by the lack of food but also by other factors such as poverty, lack of resources, restricted access to health care, rising prices of essential foods, political conflicts leading forced displacement, drought, the absence of an equity-based policy approach, poor environmental sanitation, among others. Child malnutrition should not be conceived as mainly a food-related problem. Other factors that influence this problem should also be taken into account in order to prevent it. Trained personnel should be deployed to intervene in the social determinants that underpin child malnutrition. Thus, in Colombia, the family physician is a fundamental link in the health system, responsible for providing comprehensive medical care and for the leadership of the newly introduced health model based on primary health care.

Purpose: Not much is known about the applications of artificial intelligence (AI) in cleft lip and/or palate. We aim to perform a scoping review to synthesize the literature in the last 10 years on integrating AI in the approach to this condition and highlight aspects of research into its prediction, diagnosis and treatment. Methods: A search was performed via PubMed, Science Direct, Scopus, and LILACS from 2014 to 2024, in which 649 articles were identified, and 3 studies were identified via the snowball method; the title and abstract were identified, and 35 articles were obtained for full reading. Finally, 25 studies were selected after applying the inclusion and exclusion criteria to execute this review. Results: The articles reviewed included different types of studies, with observational and experimental studies being frequent and systematic reviews and narratives being less frequent. Similarly, there was evidence of a generalized distribution, with a greater concentration in the United States. These studies were analyzed according to the use of AI applied to cleft lip/palate, obtaining 6 subcategories, including diagnosis, prediction, treatment, and education, in which different types of AI models were included, most frequently using deep learning and machine learning. Conclusion: These technologies promise to optimize the care of patients with this condition. Although current advances are promising, further research is essential to expand and refine their beneficial use. AI has driven significant advances in various stages of the cleft lip and/or palate approach, integrating tools such as assisted algorithms, genetics-based predictive models, and advanced surgical planning.